Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities

Black-box AI models are becoming increasingly common but lack transparency: As AI algorithms grow more complex and are adopted across various applications, many of these systems operate as "black boxes" where even their creators cannot easily explain how they reach specific decisions—a critical problem for building public trust in AI systems.

Explainable AI (XAI) is essential for high-stakes domains: The paper identifies XAI as a necessary solution to make AI systems transparent enough for adoption in critical areas like healthcare, criminal justice, and finance, where understanding why an AI made a decision can be as important as the decision itself.

XAI challenges span the entire AI development process: Rather than being a single problem, explainability issues emerge across multiple stages—from the initial design phase, through development, to real-world deployment—requiring solutions tailored to each stage rather than one-size-fits-all approaches.

Current XAI research directions are fragmented and scattered: The meta-survey reveals that while significant progress has been made in XAI research, the findings and recommendations are dispersed across literature, making it difficult for practitioners and policymakers to understand the full landscape of challenges and solutions available.

Future XAI work requires systematic organization: By organizing XAI challenges and research directions into coherent themes, this meta-survey provides a roadmap for advancing responsible AI adoption, suggesting that more structured approaches to XAI research are needed going forward.